code: def _measure_speed(self, func) -> float:
      try:
          if self.args.is_tpu or self.args.torchscript:
              # run additional 10 times to stabilize compilation for tpu and torchscript
              logger.info(
                  "Do inference on TPU or torchscript. Running model 5 times to stabilize compilation"
              )
              timeit.repeat(
                  func,
                  repeat=1,
                  number=5,
              )
  
          # as written in https://docs.python.org/2/library/timeit.html#timeit.Timer.repeat, min should be taken rather than the average
          runtimes = timeit.repeat(
              func,
              repeat=self.args.repeat,
              number=3,
          )
  
          if self.args.is_tpu and self.args.torch_xla_tpu_print_metrics:
              import torch_xla.debug.metrics as met
  
              self.print_fn(met.metrics_report())
  
          return min(runtimes) / 3.0
      except RuntimeError as e:
          self.print_fn("Doesn't fit on GPU. {}".format(e))
          return "N/A"
  
file_path: gluon-nlp/scripts/benchmarks/benchmark_hf.py
imports: import argparse
  import pandas
  import math
  import os
  import multiprocessing
  from multiprocessing import Process
  import torch
  from typing import Callable
  from transformers import HfArgumentParser
  from transformers.benchmark.benchmark import PyTorchBenchmark, PyTorchBenchmarkArguments
  import logging
  import timeit
label_desc: Use image node_id 3 for calling the CustomizedPyTorchBenchmark obj's underlying member method code with example usage: obj._measure_speed(func) and returns: unknown, str